The growing scale and heterogeneity of distributed energy systems has increased the complexity of their operational energy management. These systems must operate reliably under limited data and exogenous uncertainty while satisfying technical, social, and economic constraints. Classical optimization provides strong theoretical guarantees but often relies on deterministic models and point forecasts. Conversely, learning-based methods offer adaptability and scalability but may lack explicit safety handling and explainability, which are critical for deployment in safety- and flexibility-critical energy infrastructures.

Consequently, this thesis establishes an uncertainty-aware Energy Management System (EMS) architecture that preserves feasibility across planning and execution while enabling safety-constrained and human-centric intelligent control. Within a unified layered EMS structure, we address (i) exogenous uncertainty in generation and load, (ii) epistemic uncertainty arising from limited data, and (iii) exploration-related uncertainty during policy learning while supporting prosumer-in-the-loop operation. We first develop robust optimization methods with flexible and asymmetric uncertainty sets to obtain less conservative schedules while maintaining feasibility guarantees and improving economic performance. We further establish a safety-constrained reinforcement learning framework and benchmarking environment that enable systematic evaluation of performance–safety trade-offs. Finally, building on the EMS planning layer, we introduce an explainability and interaction layer in which schedules are complemented with solver-grounded explanations and what-if analyses, supporting human-in-the-loop flexibility decisions.

By jointly advancing robustness, data-efficient forecasting, safety-constrained learning, and explainable decision support, this thesis contributes an integrated and trustworthy decision-making paradigm for decentralized energy systems.

As decentralized energy resources proliferate, energy management system (EMS) design needs to handle stochastic uncertainty, hard operational constraints, and stakeholder-oriented decision support. These requirements are often addressed in isolation, resulting in a lack of integrated approaches that jointly provide constraint satisfaction, scalable operation, and transparent decision rationales.This thesis advances a unified EMS design perspective spanning three coupled dimensions: (i) robustness to multi-dimensional uncertainty, (ii) constraint-aware adaptive control, and (iii) explainable, deployable decision support for prosumer engagement. Specifically, the thesis develops an end-to-end EMS stack combining edge–cloud coordination, uncertainty-aware scheduling, safe reinforcement learning, and generative-AI explanations grounded in the underlying optimization model. Validated across microgrid, building, and energy-community case studies, the thesis establishes an empirically grounded pathway toward trustworthy, intelligent energy systems capable of accelerating the energy transition.